Restructuring Unit Tests with TestSurgeon - ICSE 2012
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TestSurgeon is a profiler that monitors the execution of unit tests to collect data on what code is being tested and how. It provides a test similarity metric and visualization to help restructure unit tests. It works by picking a "red" test method, ranking similar "blue" tests, and visualizing their execution differences. This helps define fixture initialization between similar tests and discard superfluous tests. TestSurgeon aims to reduce test size and improve maintenance by spotting differences between tests.
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- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
UiPath Test Automation using UiPath Test Suite series, part 5
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Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
By Design, not by Accident - Agile Venture Bolzano 2024
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- 1. Restructuring Unit Tests with TestSurgeon Pablo Estefó paestefo@dcc.uchile.cl Undergrad Student University of Chile Programming Languages and Environments for Intelligent, Adaptable and Distributed systems
- 2. Why restructuring unit tests? Unit tests are added when new features are added Large amount of unit tests – Long test execution – Maintenance issues – A trivial bug may invalidate many tests
- 3. My approach: TestSurgeon TestSurgeon is a profiler for unit tests Monitors execution of unit tests Collect data on what is being tested and how Provides – Test Similarity metric – Visualization
- 4. How does it work? 1) Pick a test method from a large unit tests (red test) 2) TestSurgeon ranks similar tests (blue test) 3) Visualize the tests execution and their differences
- 5. Test Execution Blueprint methods Called by Red and Blue test Called by Blue test Called by Red test a base class
- 6. Scenario #1: Defining Fixture Initialization Two test methods in the same unit test Each test define its own test scenario and they are similar
- 7. Example: Scenario #1 MOFormsBuilderTest>>testElementAt (Red test) MOFormsBuilderTest>>testDisplaying (Blue test)
- 8. Example: Scenario #1 MOFormsBuilderTest>>testElementAt (Red test) MOFormsBuilderTest>>testDisplaying (Blue test) Solution: Move what calls the yellow portion in a setUp method => test size reduction
- 9. Scenario #2: Discarding superfluous test method Small portions of blue or red but not both Yellow, large and squared shaped methods
- 10. Example: Scenario #2 MOLayoutTest>>testHorizontalLineLayout (Red test) MOViewRendererTest>>testCenter (Blue test)
- 11. Example: Scenario #2 MOLayoutTest>>testHorizontalLineLayout (Red test) MOViewRendererTest>>testCenter (Blue test) Solution: Do not consider the blue test for frequent testing
- 12. Test Surgeon Profiler for unit tests Useful to spot differences Help to restructure tests Future Work: More accurate comparison of execution Comparing groups of tests State of object receivers Message arguments Side effect Line coverage
- 13. |𝑇 𝑟 ∩ 𝑇 𝑏 | 𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑡 𝑟 , 𝑡 𝑏 = |𝑇 𝑟 ∪ 𝑇 𝑏 | Pablo Estefó - paestefo@dcc.uchile.cl